ArticleIEEE transactions on medical imaging2019
Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 2 of them syntheses that pooled it.
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Who cites it
59 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for cephalometric landmark detection: systematic review and meta-analysis.Clinical oral investigations · 2021Pooled it
- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Artificial intelligence in breast cancer imaging: a systematic review of diagnostic performance, predictive modeling, and clinical translation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Deep learning-based biliary stent classification and transfer learning adaptation to an additional stent type.European radiology experimental · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images.Scientific reports · 2025Article
- scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.Communications biology · 2025Article
- Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer.Life (Basel, Switzerland) · 2025Article
- Combining shallow and deep neural networks on pseudo-color enhanced images for digital breast tomosynthesis lesion classification.Frontiers in digital health · 2025Article
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- Pre-Reconstruction Processing with the Cycle-Consist Generative Adversarial Network Combined with Attention Gate to Improve Image Quality in Digital Breast Tomosynthesis.Diagnostics (Basel, Switzerland) · 2024Article
- Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data.Diagnostics (Basel, Switzerland) · 2024Article
- Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.Physics in medicine and biology · 2023Article
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Ipsilateral Lesion Detection Refinement for Tomosynthesis.IEEE transactions on medical imaging · 2023Article
- Article
- A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis.Scientific reports · 2023Article
- Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.Journal of medical imaging (Bellingham, Wash.) · 2023Article
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6 authors.
Funding
Abstract
In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage transfer learning approach that utilized data from similar auxiliary domains for intermediate-stage fine-tuning. Breast imaging data from DBT, digitized screen-film mammography, and digital mammography totaling 4039 unique regions of interest (1797 malignant and 2242 benign) were collected. Using cross validation, we selected the best transfer network from six transfer networks by varying the level up to which the convolutional layers were frozen. In a single-stage transfer learning approach, knowledge from CNN trained on the ImageNet data was fine-tuned directly with the DBT data. In a multi-stage transfer learning approach, knowledge learned from ImageNet was first fine-tuned with the mammography data and then fine-tuned with the DBT data. Two transfer networks were compared for the second-stage transfer learning by freezing most of the CNN structures versus freezing only the first convolutional layer. We studied the dependence of the classification performance on training sample size for various transfer learning and fine-tuning schemes by varying the training data from 1% to 100% of the available sets. The area under the receiver operating characteristic curve (AUC) was used as a performance measure. The view-based AUC on the test set for single-stage transfer learning was 0.85 ± 0.05 and improved significantly (p <; 0.05$ ) to 0.91 ± 0.03 for multi-stage learning. This paper demonstrated that, when the training sample size from the target domain is limited, an additional stage of transfer learning using data from a similar auxiliary domain is advantageous.
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